Incremental Eigenspace Model Applied to Real World Problem
Byung Joo Kim · Advanced science and technology letters · 2015
Recent years have witnessed a dramatic increase in our ability to collect data from various sensors, devices, in different formats, from independent or connected applications. This data flood has outpaced our capability to process, analyze, store and understand these datasets. Many machine learning algorithms do not scale beyond data sets of a few million elements or cannot tolerate the statistical noise and gaps found in real-world data. Further research is required to develop algorithms that apply in real-world situations and on data sets of trillions of elements. In this paper we propose a scalable algorithm to handle the huge collections of data. Through the experimental results, proposed method performs well on huge data from UCI machine learning repository data Set.